Stochastic model of production and inventory control using dynamic bayesian network

Ji Sun Shin, Tae Hong Lee, Jin Il Kim, HeeHyol Lee

研究成果: Article

3 引用 (Scopus)

抄録

Bayesian Network is a stochastic model, which shows the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. This paper deals with the production and inventory control using the dynamic Bayesian network. The probabilistic values of the amount of delivered goods and the production quantities are changed in the real environment, and then the total stock is also changed randomly. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the Bayesian network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound and the upper bound of the total stock to certain values is shown.

元の言語English
ページ(範囲)148-154
ページ数7
ジャーナルArtificial Life and Robotics
13
発行部数1
DOI
出版物ステータスPublished - 2008

Fingerprint

Inventory control
Production control
Bayesian networks
Stochastic models
Equipment and Supplies
Epidemiologic Effect Modifiers
Random variables

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biochemistry, Genetics and Molecular Biology(all)

これを引用

Stochastic model of production and inventory control using dynamic bayesian network. / Shin, Ji Sun; Lee, Tae Hong; Kim, Jin Il; Lee, HeeHyol.

:: Artificial Life and Robotics, 巻 13, 番号 1, 2008, p. 148-154.

研究成果: Article

Shin, Ji Sun ; Lee, Tae Hong ; Kim, Jin Il ; Lee, HeeHyol. / Stochastic model of production and inventory control using dynamic bayesian network. :: Artificial Life and Robotics. 2008 ; 巻 13, 番号 1. pp. 148-154.
@article{49044e98604440bb96fd909f241512fb,
title = "Stochastic model of production and inventory control using dynamic bayesian network",
abstract = "Bayesian Network is a stochastic model, which shows the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. This paper deals with the production and inventory control using the dynamic Bayesian network. The probabilistic values of the amount of delivered goods and the production quantities are changed in the real environment, and then the total stock is also changed randomly. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the Bayesian network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound and the upper bound of the total stock to certain values is shown.",
keywords = "Dynamic Bayesian Network, Graphical Modeling, Probability distribution, Production inventory control",
author = "Shin, {Ji Sun} and Lee, {Tae Hong} and Kim, {Jin Il} and HeeHyol Lee",
year = "2008",
doi = "10.1007/s10015-008-0581-x",
language = "English",
volume = "13",
pages = "148--154",
journal = "Artificial Life and Robotics",
issn = "1433-5298",
publisher = "Springer Japan",
number = "1",

}

TY - JOUR

T1 - Stochastic model of production and inventory control using dynamic bayesian network

AU - Shin, Ji Sun

AU - Lee, Tae Hong

AU - Kim, Jin Il

AU - Lee, HeeHyol

PY - 2008

Y1 - 2008

N2 - Bayesian Network is a stochastic model, which shows the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. This paper deals with the production and inventory control using the dynamic Bayesian network. The probabilistic values of the amount of delivered goods and the production quantities are changed in the real environment, and then the total stock is also changed randomly. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the Bayesian network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound and the upper bound of the total stock to certain values is shown.

AB - Bayesian Network is a stochastic model, which shows the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. This paper deals with the production and inventory control using the dynamic Bayesian network. The probabilistic values of the amount of delivered goods and the production quantities are changed in the real environment, and then the total stock is also changed randomly. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the Bayesian network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound and the upper bound of the total stock to certain values is shown.

KW - Dynamic Bayesian Network

KW - Graphical Modeling

KW - Probability distribution

KW - Production inventory control

UR - http://www.scopus.com/inward/record.url?scp=58049216458&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=58049216458&partnerID=8YFLogxK

U2 - 10.1007/s10015-008-0581-x

DO - 10.1007/s10015-008-0581-x

M3 - Article

AN - SCOPUS:58049216458

VL - 13

SP - 148

EP - 154

JO - Artificial Life and Robotics

JF - Artificial Life and Robotics

SN - 1433-5298

IS - 1

ER -